The invention relates to portfolio structuring and, more particularly, to structuring of a portfolio including assets/liabilities such as real estate, stocks, bonds, futures, options, collateralized mortgage obligations, single-premium deferred annuities and insurance contracts.
Portfolio structuring includes transactions such as buying, holding and selling of assets/liabilities, as well the adoption, continuation, modification or termination of transaction strategies. Typically, decisions concerning transactions and strategies are based on the evaluation of one or several complicated functions of a large number of variables such as a multi-dimensional integral or the inverse of a distribution function, for example.
In financial securities transactions, which includes the initial sale, the value of a security may be estimated, e.g., based on expected future cash flow. Such cash flow may depend on variable interest rates, for example, and these and other relevant variables may be viewed as stochastic variables.
It is well known that the value of a financial security which depends on stochastic variables can be estimated in terms of a multi-dimensional integral. The dimension of such an integral may be very high.
In collateralized mortgage obligations (CMO), for example, instruments or securities variously called tranches, shares, participations, classes or contracts have cash flows which are determined by dividing and distributing the cash flow of an underlying collection or pool of mortgages on a monthly basis according to pre-specified rules. The present value of a tranche can be estimated on the basis of the expected monthly cash flows over the remaining term of the tranche, and an estimate of the present value of a tranche can be represented as a multi-dimensional integral whose dimension is the number of payment periods of the tranche. For a typical instrument with a 30-year term and with monthly payments, this dimension is 360.
Usually, such a high-dimensional integral can be evaluated only approximately, by numerical integration. This involves the generation of points in the domain of integration, evaluating or "sampling" the integrand at the generated points, and combining the resulting integrand values, e.g., by averaging. Well known for numerical integration in securities valuation is the so-called Monte Carlo method in which points in the domain of integration are generated at random.
With integrands arising in financial securities valuation, the computational work in combining the sampled values is negligible as compared with producing the integrand values. Thus, numerical integration methods in securities valuation may be compared based on the number of samples required for obtaining a sufficiently accurate approximation to the integral.
Sampling techniques are useful also as applied to formulations which need not be in terms of an integral. In particular, in portfolio structuring, this applies to a quantity known as value at risk (VAR) which may be defined as the worst potential loss of value of a portfolio over a period of time under normal market conditions, at a specified confidence level. While value at risk can be expressed as a definite limit in a one-sided indefinite integral (see Philippe Jorion, Value at Risk, Irwin Professional Publishing, 1997, p. 88), a more practical formulation is in terms of the inverse of a cumulative distribution function.